AI — Uninformed Search — Broad Search Algorithm (BFS) (in Arabic)

BFS algorithm is like exploring a city without a map – just wandering around until you find what you’re looking for. It’s like going on a wild goose chase! But, hey, sometimes the journey is more important than the destination. Just don’t get lost! 🗺️🔍

Key Takeaways

Guarantees shortest pathRequires more memory
Easily implementedRequires a lot of time
Visits all neighbors equallyInefficient for large data sets

Breadth First Search (BFS) algorithm is a crucial component of AI’s uniformed search technique. This algorithm explores all the neighbor nodes at the present level before moving on to the nodes at the next level, guaranteeing the shortest path between the source and target nodes. It is easily implemented and ensures that all neighbors are visited equally. However, BFS requires a significant amount of memory and can be inefficient for large datasets.

Understanding BFS

📚 Basics of BFS

BFS searches level by level, focusing on all the nodes at the current level, and then moving on to the nodes at the next level.

🏁 Purpose of BFS

The primary purpose of BFS is to guarantee the shortest path between a source and a target node.

BFS Implementation

🖥️ Exploring All Neighbors

One of the key features of BFS is that it explores all the neighbors of a node before moving on to the next level, ensuring comprehensive coverage.

⏳ Time and Memory Requirements

While BFS is easily implemented, it requires more memory and can be inefficient for large datasets, especially in comparison to other search algorithms.


In conclusion, Breadth First Search (BFS) algorithm is a valuable tool in AI’s uniformed search technique due to its ability to guarantee the shortest path. However, its memory and time requirements should be carefully considered, especially when working with large data sets. With its level-by-level exploration, BFS provides an efficient and comprehensive approach to search problems.

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